README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:268861
- loss:MultipleNegativesRankingLoss
base_model: Qwen/Qwen3-0.6B-Base
widget:
- source_sentence: 'There are seven thieves. They stole diamonds from a diamond merchant
    and ran away. While running, night sets in and they decide to rest in the jungle.

    When everybody was sleeping, two of them woke up and decided to divide the diamonds
    equally among themselves. But when they divided the diamonds equally, one diamond
    is left.

    So they woke up the 3rd thief and tried to divide the diamonds equally again but
    still one diamond was left. Then they woke up the 4th thief to divide the diamonds
    equally again, and again one diamond was left. This happened with the 5th and
    6th thief – one diamond was still left.

    Finally, they woke up the 7th thief and this time the diamonds were divided equally.

    How many diamonds did they steal in total?'
  sentences:
  - ''''
  - ''''
  - e
- source_sentence: 'praveen starts business with rs . 3220 and after 5 months , hari
    joins with praveen as his partner . after a year , the profit is divided in the
    ratio 2 : 3 . what is hari ’ s contribution in the capital ?'
  sentences:
  - s
  - '5'
  - '['
- source_sentence: 'Which of the following is material of choice in class V

    cavity with abfraction?'
  sentences:
  - '['
  - t
  - G
- source_sentence: A right circular cylinder has a height of 25 and a radius of 5.
    A rectangular solid with a height of 15 and a square base, is placed in the cylinder
    such that each of the corners of the solid is tangent to the cylinder wall. Liquid
    is then poured into the cylinder such that it reaches the rim. What is the volume
    of the liquid?
  sentences:
  - '5'
  - '['
  - '2'
- source_sentence: Cerebral angiography was performed by -
  sentences:
  - S
  - t
  - '2'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on Qwen/Qwen3-0.6B-Base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) <!-- at revision 11214f7f3465775dcce23c3752ecea5a42ee0ddc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: Qwen3Model 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Cerebral angiography was performed by -',
    'S',
    '2',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 268,861 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                      |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 48.3 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0 tokens</li><li>mean: 0.97 tokens</li><li>max: 1 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                    | sentence_1     |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>A 1200 m long train crosses a tree in 120 sec, how much time will I take to pass a platform 1100 m long?</code>                                         | <code>'</code> |
  | <code>What is the opposite of rarefaction zones, where air molecules in waves are loosely packed?</code>                                                      | <code>[</code> |
  | <code>if w is 40 percent less than e , e is 40 percent less than y , and z is 46 percent less than y , then z is greater than w by what percent of w ?</code> | <code>%</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 4
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.1190 | 500   | 4.0939        |
| 0.2380 | 1000  | 3.7716        |
| 0.3571 | 1500  | 0.0           |
| 0.4761 | 2000  | 0.0           |
| 0.5951 | 2500  | 0.0           |
| 0.7141 | 3000  | 0.0           |
| 0.8331 | 3500  | 0.0           |
| 0.9522 | 4000  | 0.0           |
| 1.0712 | 4500  | 0.0           |
| 1.1902 | 5000  | 0.0           |
| 1.3092 | 5500  | 0.0           |
| 1.4282 | 6000  | 0.0           |
| 1.5473 | 6500  | 0.0           |
| 1.6663 | 7000  | 0.0           |
| 1.7853 | 7500  | 0.0           |
| 1.9043 | 8000  | 0.0           |
| 2.0233 | 8500  | 0.0           |
| 2.1423 | 9000  | 0.0           |
| 2.2614 | 9500  | 0.0           |
| 2.3804 | 10000 | 0.0           |
| 2.4994 | 10500 | 0.0           |
| 2.6184 | 11000 | 0.0           |
| 2.7374 | 11500 | 0.0           |
| 2.8565 | 12000 | 0.0           |
| 2.9755 | 12500 | 0.0           |
| 3.0945 | 13000 | 0.0           |
| 3.2135 | 13500 | 0.0           |
| 3.3325 | 14000 | 0.0           |
| 3.4516 | 14500 | 0.0           |
| 3.5706 | 15000 | 0.0           |
| 3.6896 | 15500 | 0.0           |
| 3.8086 | 16000 | 0.0           |
| 3.9276 | 16500 | 0.0           |


### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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